






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_						:						Union[str, Any]						      =					logging.get_logger(__name__)

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							List[Any]:
  UpperCamelCase_:   Optional[int]        =							[]
  for i in range(config.num_hidden_layers    ):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''deit.encoder.layer.{i}.layernorm_before.weight''')    )
    rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''deit.encoder.layer.{i}.layernorm_before.bias''')    )
    rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''deit.encoder.layer.{i}.attention.output.dense.weight''')    )
    rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''deit.encoder.layer.{i}.attention.output.dense.bias''')    )
    rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''deit.encoder.layer.{i}.layernorm_after.weight''')    )
    rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''deit.encoder.layer.{i}.layernorm_after.bias''')    )
    rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''deit.encoder.layer.{i}.intermediate.dense.weight''')    )
    rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''deit.encoder.layer.{i}.intermediate.dense.bias''')    )
    rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''deit.encoder.layer.{i}.output.dense.weight''')    )
    rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''deit.encoder.layer.{i}.output.dense.bias''')    )

  # projection layer + position embeddings
  rename_keys.extend(
      [
          ('cls_token', 'deit.embeddings.cls_token'),
          ('dist_token', 'deit.embeddings.distillation_token'),
          ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
          ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
          ('pos_embed', 'deit.embeddings.position_embeddings'),
      ]    )

  if base_model:
    # layernorm + pooler
    rename_keys.extend(
        [
            ('norm.weight', 'layernorm.weight'),
            ('norm.bias', 'layernorm.bias'),
            ('pre_logits.fc.weight', 'pooler.dense.weight'),
            ('pre_logits.fc.bias', 'pooler.dense.bias'),
        ]    )

    # if just the base model, we should remove "deit" from all keys that start with "deit"
    UpperCamelCase_:   str        =							[(pair[0], pair[1][4:]) if pair[1].startswith('deit'    ) else pair for pair in rename_keys]
  else:
    # layernorm + classification heads
    rename_keys.extend(
        [
            ('norm.weight', 'deit.layernorm.weight'),
            ('norm.bias', 'deit.layernorm.bias'),
            ('head.weight', 'cls_classifier.weight'),
            ('head.bias', 'cls_classifier.bias'),
            ('head_dist.weight', 'distillation_classifier.weight'),
            ('head_dist.bias', 'distillation_classifier.bias'),
        ]    )

  return rename_keys

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__=False    )			->							Optional[int]:
  for i in range(config.num_hidden_layers    ):
    if base_model:
      UpperCamelCase_:   Tuple        =							''
    else:
      UpperCamelCase_:   List[Any]        =							'deit.'
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    UpperCamelCase_:   Dict        =							state_dict.pop(F'''blocks.{i}.attn.qkv.weight'''    )
    UpperCamelCase_:   List[str]        =							state_dict.pop(F'''blocks.{i}.attn.qkv.bias'''    )
    # next, add query, keys and values (in that order) to the state dict
    UpperCamelCase_:   int        =							in_proj_weight[
        : config.hidden_size, :
    ]
    UpperCamelCase_:   List[Any]        =							in_proj_bias[: config.hidden_size]
    UpperCamelCase_:   Optional[int]        =							in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    UpperCamelCase_:   int        =							in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    UpperCamelCase_:   Optional[int]        =							in_proj_weight[
        -config.hidden_size :, :
    ]
    UpperCamelCase_:   int        =							in_proj_bias[-config.hidden_size :]

def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )			->							Union[str, Any]:
  UpperCamelCase_:   List[str]        =							dct.pop(UpperCAmelCase__    )
  UpperCamelCase_:   str        =							val

def     snake_case   ()			->							Any:
  UpperCamelCase_:   int        =							'http://images.cocodataset.org/val2017/000000039769.jpg'
  UpperCamelCase_:   int        =							Image.open(requests.get(UpperCAmelCase__      ,       stream=UpperCAmelCase__    ).raw    )
  return im

@torch.no_grad()
def     snake_case   (UpperCAmelCase__      ,       UpperCAmelCase__    )			->							int:
  UpperCamelCase_:   Optional[int]        =							DeiTConfig()
  # all deit models have fine-tuned heads
  UpperCamelCase_:   Optional[int]        =							False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  UpperCamelCase_:   Optional[Any]        =							1_0_0_0
  UpperCamelCase_:   Any        =							'huggingface/label-files'
  UpperCamelCase_:   str        =							'imagenet-1k-id2label.json'
  UpperCamelCase_:   Optional[int]        =							json.load(open(hf_hub_download(UpperCAmelCase__      ,       UpperCAmelCase__      ,       repo_type='dataset'    )      ,       'r'    )    )
  UpperCamelCase_:   Any        =							{int(UpperCAmelCase__    ): v for k, v in idalabel.items()}
  UpperCamelCase_:   List[Any]        =							idalabel
  UpperCamelCase_:   Dict        =							{v: k for k, v in idalabel.items()}
  UpperCamelCase_:   Optional[Any]        =							int(deit_name[-6:-4]    )
  UpperCamelCase_:   Union[str, Any]        =							int(deit_name[-3:]    )
  # size of the architecture
  if deit_name[9:].startswith('tiny'    ):
    UpperCamelCase_:   Union[str, Any]        =							1_9_2
    UpperCamelCase_:   str        =							7_6_8
    UpperCamelCase_:   Dict        =							1_2
    UpperCamelCase_:   Dict        =							3
  elif deit_name[9:].startswith('small'    ):
    UpperCamelCase_:   Union[str, Any]        =							3_8_4
    UpperCamelCase_:   Tuple        =							1_5_3_6
    UpperCamelCase_:   Optional[Any]        =							1_2
    UpperCamelCase_:   Optional[Any]        =							6
  if deit_name[9:].startswith('base'    ):
    pass
  elif deit_name[4:].startswith('large'    ):
    UpperCamelCase_:   int        =							1_0_2_4
    UpperCamelCase_:   Optional[Any]        =							4_0_9_6
    UpperCamelCase_:   Any        =							2_4
    UpperCamelCase_:   Optional[int]        =							1_6

  # load original model from timm
  UpperCamelCase_:   Tuple        =							timm.create_model(UpperCAmelCase__      ,       pretrained=UpperCAmelCase__    )
  timm_model.eval()

  # load state_dict of original model, remove and rename some keys
  UpperCamelCase_:   Tuple        =							timm_model.state_dict()
  UpperCamelCase_:   Any        =							create_rename_keys(UpperCAmelCase__      ,       UpperCAmelCase__    )
  for src, dest in rename_keys:
    rename_key(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )
  read_in_q_k_v(UpperCAmelCase__      ,       UpperCAmelCase__      ,       UpperCAmelCase__    )

  # load HuggingFace model
  UpperCamelCase_:   str        =							DeiTForImageClassificationWithTeacher(UpperCAmelCase__    ).eval()
  model.load_state_dict(UpperCAmelCase__    )

  # Check outputs on an image, prepared by DeiTImageProcessor
  UpperCamelCase_:   Union[str, Any]        =							int(
      (2_5_6 / 2_2_4) * config.image_size    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  UpperCamelCase_:   int        =							DeiTImageProcessor(size=UpperCAmelCase__      ,       crop_size=config.image_size    )
  UpperCamelCase_:   List[str]        =							image_processor(images=prepare_img()      ,       return_tensors='pt'    )
  UpperCamelCase_:   Optional[Any]        =							encoding['pixel_values']
  UpperCamelCase_:   int        =							model(UpperCAmelCase__    )

  UpperCamelCase_:   List[str]        =							timm_model(UpperCAmelCase__    )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase__      ,       outputs.logits      ,       atol=1E-3    )

  Path(UpperCAmelCase__    ).mkdir(exist_ok=UpperCAmelCase__    )
  print(F'''Saving model {deit_name} to {pytorch_dump_folder_path}'''    )
  model.save_pretrained(UpperCAmelCase__    )
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''    )
  image_processor.save_pretrained(UpperCAmelCase__    )


if __name__ == "__main__":
   A_						:						Union[str, Any]						      =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--deit_name',
       default='vit_deit_base_distilled_patch16_224',
       type=str,
       help='Name of the DeiT timm model you\'d like to convert.',
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
   )

   A_						:						int						      =					parser.parse_args()
   convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCAmelCase				:     Optional[int]					=				{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     int					=				[
					    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """SwinForImageClassification""",
					    """SwinForMaskedImageModeling""",
					    """SwinModel""",
					    """SwinPreTrainedModel""",
					    """SwinBackbone""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase				:     str					=				[
					    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFSwinForImageClassification""",
					    """TFSwinForMaskedImageModeling""",
					    """TFSwinModel""",
					    """TFSwinPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swin import (
										    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwinBackbone,
										    SwinForImageClassification,
										    SwinForMaskedImageModeling,
										    SwinModel,
										    SwinPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_swin import (
										    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFSwinForImageClassification,
										    TFSwinForMaskedImageModeling,
										    TFSwinModel,
										    TFSwinPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase				:     int					=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



